ISPOR Atlanta 2024

We were truly inspired by our experience at ISPOR 2024 in Atlanta this past week. We extend our heartfelt gratitude to the entire ISPOR team for their unwavering dedication in orchestrating such a remarkable event. 

The conference illuminated a transformative vision for healthcare: 'A world where healthcare is accessible, effective, efficient, and affordable for all.' This vision was powerfully demonstrated in the sessions dedicated to Patient-Centred Research, which underscored the profound impact of patient perspectives on enhancing health outcomes.

One of the standout features of this year's conference was the commitment to integrating the industry with better means to improve efficiency and quality across all areas of healthcare through:‍

1. Emphasis on Artificial Intelligence (AI) and Machine Learning (ML):

The multiple sessions speaking on AI and ML illustrate a significant focus on incorporating AI and machine learning into healthcare. These sessions aim to streamline evidence generation and improve market access, highlighting the industry's dedication to using cutting-edge technology to enhance the efficiency and quality of healthcare delivery.

2. Innovative Approaches to Health Technology Assessment and Real-World Evidence.

Health Technology Assessments and Real-World Evidence sessions undoubtedly reflected a commitment to integrating advanced methodologies to enhance decision-making processes. These sessions underscored the industry's focus on leveraging real-world data and network meta-analysis to improve the accuracy and relevance of health technology assessments, ultimately leading to more efficient and higher-quality healthcare outcomes.

3. The Integration of Patient-Centred Outcomes and Health Equity

These sessions underscored the vital roles that patients and caregivers play in research, highlighting their dual positions as both engaged research partners and participants. They detailed how patient and caregiver engagement forms are adapted to fit specific study objectives, practical considerations, and intended outcomes. This emphasis showcased the need for flexible and inclusive approaches to effectively incorporate patient insights and experiences.

At the conference Laser AI revealed our poster: Health states utilities for allergic rhinitis - an AI-supported systematic review.

Allergic rhinitis (AR) is a prevalent health issue affecting many people globally, primarily due to factors like urbanization, lifestyle changes, and environmental conditions. It significantly disrupts daily life and causes considerable discomfort. Health state utility values (HSUVs) are critical in health economics and outcomes research, as they quantify individuals' preferences for different health states. These values are instrumental in evaluating the burden of AR and determining the cost-effectiveness of various treatments, thereby aiding healthcare decision-makers in optimizing resource allocation and maximizing health outcomes within budget constraints. Artificial intelligence can potentially enhance the efficiency and accuracy of systematic reviews concerning HSUVs, promoting evidence-based decision-making in healthcare.

The study's objective was to create a comprehensive catalogue of HSUVs for children and adults diagnosed with AR, identify evidence gaps, and suggest future research directions. A systematic search identified nine studies encompassing 15,609 patients, providing HSUVs for 58 health states. Most research was conducted in Europe and North America, with no data from South America, Africa, or Australia. The studies mainly focused on adults, employing instruments like EQ-5D, Standard Gamble, and Time Trade-Off to elicit HSUVs. The quality assessment indicated no significant concerns regarding participant selection and data analysis, although the general risk of bias was high. The catalogue includes HSUVs for seasonal AR (SAR), perennial AR (PAR), unspecified AR, and individual symptoms. The analysis revealed that higher AR severity and concomitant asthma lead to lower utility values, and children reported lower HSUVs compared to adults. This dataset of HSUVs is crucial for supporting future economic studies, with AI playing a key role in streamlining the data processing. However, further research is necessary to explore HSUVs in children and patients from underrepresented regions.

As a participant at the ISPOR Atlanta 2024 conference, Laser AI found the sessions to be not only informative but also inspiring and encouraging. The innovative approach to AI in evidence synthesis was particularly enlightening, and the insights gained from these sessions will undoubtedly shape our future endeavours in the healthcare industry.